Content delivery networks (CDNs) have greatly improved the way content is transferred across data networks such as the Internet. A CDN accelerates the delivery of content by reducing the distance that content travels in order to reach a destination. To do so, the CDN strategically locates surrogate origin servers, also referred to as caching servers or edge servers, at various points-of-presence (PoPs) that are geographically proximate to large numbers of content consumers and the CDN utilizes a traffic management system to route requests for content hosted by the CDN to the edge server that can optimally deliver the requested content to the content consumer. As used hereafter optimal delivery of content refers to the most efficient available means with which content can be delivered from a server to an end user machine over a data network. Optimal delivery of content can be quantified in terms of latency, jitter, packet loss, distance, and overall end user experience.
Determination of the optimal edge server may be based on geographic proximity to the content consumer as well as other factors such as load, capacity, and responsiveness of the edge servers. The optimal edge server delivers the requested content to the content consumer in a manner that is more efficient than when origin servers of the content provider deliver the requested content. For example, a CDN may locate edge servers in Los Angeles, Dallas, and New York. These edge servers may cache content that is published by a particular content provider with an origin server in Miami. When a content consumer in San Francisco submits a request for the published content, the CDN will deliver the content from the Los Angeles edge server on behalf of the content provider as opposed to the much greater distance that would be required when delivering the content from the origin server in Miami. In this manner, the CDN reduces the latency, jitter, and amount of buffering that is experienced by the content consumer. The CDN also allows the content provider to offload infrastructure, configuration, and maintenance costs while still having the ability to rapidly scale resources as needed. Content providers can therefore devote more time to the creation of content and less time to the creation of an infrastructure that delivers the created content to the content consumers.
As a result of these and other benefits, many different CDNs are in operation today. Edgecast, Akamai, Limelight, and CDNetworks are some examples of operating CDNs. One of the key differentiators between the CDNs is performance. Based on routing techniques, number of PoPs, load, etc., some CDNs are able to consistently provide better performance when delivering content to end users than other CDNs. The improved performance may be on the order of tens or hundreds of milliseconds. For certain content, such as streaming content, the improved performance can make a noticeable difference for the end user experience. This performance difference can also be a determinant factor as to whether an end user completes a commercial transaction, subscribes for a service or content, or stays on the site of one content provider or visits a site of another content provider.
To differentiate itself from other CDNs on the basis of performance, Edgecast utilizes Anycast routing. Anycast is a routing methodology that is well known in the art in which a content request is routed to the topologically nearest PoP from a group of PoPs that are all identified with the same destination address, wherein each PoP of the group of PoPs contains at least one server that can serve the content requested by the end user. In this manner, Edgecast resolves end user requests to servers within its CDN that can optimally serve the requested content to the end user.
While use of Anycast routing within the Edgecast CDN has proven to provide performance improvements over other routing methodologies used by other CDNs, the decentralized and distributed control over Anycast routing renders existing monitoring tools unsuitable for Anycast performance analysis. To accurately monitor Anycast performance in a CDN environment, it is necessary to derive performance results from the perspective of the CDN end users, as it may be the case that in certain scenarios Anycast routing does not produce optimal routing for certain end users that request content from the CDN. For example, servers of a first PoP being routed to by Anycast routing may deliver content to some end users less optimally than servers of a second PoP.
Furthermore, a fully optimized CDN is one that is optimized from end-to-end. End-to-end optimization involves optimizing the entire end user experience from the routing of the end user to an optimal PoP to the optimized delivery of the requested content from the optimal PoP to the end user. The latter portion of the end user experience depends partly on the ability of a transit provider or peering neighbor connecting the PoP to the end user to deliver the content in an efficient manner. A CDN can utilize the routes of different transit providers and the routes of different peering neighbors to connect to the end users. The different routes are subject to different loads, bandwidth, delay, packet loss, etc. Therefore, to fully optimize the CDN service, it is necessary to monitor the performance of the different routes and to select the route that delivers content from a particular PoP of the CDN to a particular end user in the most efficient manner.
Accordingly, there is a need to monitor Anycast performance in a CDN to identify optimization opportunities for the CDN whereby content delivery from the CDN to the end user can be improved in terms of one or more of latency, jitter, packet loss, distance, and overall end user experience. Specifically, there is a need to perform such monitoring from the end users perspective in order to ascertain whether the Anycast selected PoP or server is the optimal PoP or server for the end user and to optimize Anycast routing when the selected PoP or server is not optimally selected by Anycast routing. There is further a need to monitor the performance of the different transit provider routes and the different peering neighbor routes connecting end users to the CDN services and to select the most efficient route for delivering content from different PoPs to different sets of end users.